lifelines 0.2.2.7

[What is survival analysis and why should I learn it?](http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html) Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer *why do events occur now versus later* under uncertainity (where *events* might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like *what factors might influence deaths?*

But outside of medicine and actuarial science, there are many other interesting and exciting applications of this lesser-known technique, for example:- SaaS providers are interested in measuring customer lifetimes, or time to first behaviours.- sociologists are interested in measure political parties lifetimes, or relationships, or marriages- Businesses are interested in what variables affect lifetime value

*lifelines* is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using *lifelines*, please ping me at [@cmrn_dp](https://twitter.com/Cmrn_DP) and let me know your thoughts on the library.

If you are new to survival analysis, wondering why it is useful, or are interested in *lifelines* examples and syntax,please check out the [Documentation and Tutorials page](http://lifelines.readthedocs.org/en/latest/index.html)

Alternatively, you can **use the IPython notebooks tutorials**, located in the main directory of the repo:

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